Fission product yields are key infrastructure data for nuclear applications in many aspects. It is a challenge both experimentally and theoretically to obtain accurate and complete energy-dependent fission yields. We apply the Bayesian neural network (BNN) approach to learn existed fission yields and predict unknowns with uncertainty quantification. We demonstrated that BNN is particularly useful for evaluations of fission yields when incomplete experimental data are available. The BNN results are quite satisfactory on distribution positions and energy dependencies of fission yields.

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Charge transfer plays a key role at the interfaces of heterostructures, which can affect electronic structures and ultimately the physical properties of the materials. However, charge transfer is difficult to manipulate externally once the interface is formed. The recently discovered van der Waals ferromagnets with atomically sharp interfaces provided a perfect platform for the electrical control of interfacial charge transfer. Here, we report magnetoresistance experiments revealing electrically tunable charge transfer in Fe 3 GeTe 2 /Cr 2 Ge 2 Te 6 /Fe 3 GeTe 2 all-magnetic van der Waals heterostructures, which can be exploited to selectively modify the switching fields of the top or bottom Fe 3 GeTe 2 electrodes. The directional charge transfer from metallic Fe 3 GeTe 2 to semiconducting Cr 2 Ge 2 Te 6 is revealed by first-principles calculations, which remarkably modifies the magnetic anisotropy energy of Fe 3 GeTe 2 , leading to the dramatically suppressed coercivity. The electrically selective control of magnetism demonstrated in this study could stimulate the development of spintronic devices based on van der Waals magnets. KEYWORDS: controllable coercivity, charge transfer, Cr 2 Ge 2 Te 6 , Fe 3 GeTe 2 , van der Waals heterostructure